For the modeling of the photometric and RV data in this work, we used juliet [1]}, a Python-based fitting package that applies nested samplers to explore efficiently the parameter space of a given prior volume and compute the Bayesian model log evidence, \(\ln \mathcal {Z}\) . We used dynesty [2]} as our dynamic nested sampling algorithm. The juliet package is built on many publicly available tools for the modeling of transits [3]}, RVs [4]}, and GPs (george, [5]}; celerite, [6]}). Due to its efficient computation of the \(\ln \mathcal {Z}\) , we can compare statistically models with different numbers of parameters accounting for the model complexity and the number of degrees of freedom. We consider a model to be moderately favored over another if the difference in its Bayesian log evidence is greater than two, and strongly favored if it is greater than five [7]}. If \(\Delta \ln \mathcal {Z} \lesssim 2\) , then the models are indistinguishable so the simpler model with fewer degrees of freedom would be chosen.
